Metadata-Version: 1.2
Name: PyTorch-ProbGraph
Version: 0.0.1
Summary: Hierarchical Probabilistic Graphical Models in PyTorch
Home-page: https://github.com/kpoeppel/pytorch_probgraph/
Author: Korbinian Poeppel, Hendrik Elvers
Author-email: korbinian.poeppel@tum.de, hendrik.elvers@tum.de
License: UNKNOWN
Description: # README of "PyTorch-ProbGraph"
        
        ## What is PyTorch-ProbGraph?
        
        PyTorch-ProbGraph is a library based on amazing PyTorch (https://pytorch.org)
        to easily use and adapt directed and undirected Hierarchical Probabilistic
        Graphical Models. These include Restricted Boltzmann Machines,
        Deep Belief Networks, Deep Boltzmann Machines and Helmholtz
        Machines (Sigmoid Belief Networks).
        
        Models can be set up in a modular fashion, using UnitLayers, layers of Random Units and Interactions between these UnitLayers.
        Currently, only Gaussian, Categorical and Bernoulli units are available, but an extension can be made to allow all kinds of distributions from the Exponential family.
        (see https://en.wikipedia.org/wiki/Exponential_family)
        
        The Interactions are usually only linear for undirected models, but can be built
        from arbitrary PyTorch torch.nn.Modules (using forward and the backward gradient).
        
        There is a pre-implemented fully-connected InteractionLinear, one for using
        existing torch.nn.Modules and some custom Interactions / Mappings to enable
        Probabilistic Max-Pooling. Interactions can also be connected without intermediate
        Random UnitLayers with InteractionSequential.
        
        This library was built by Korbinian Poeppel and Hendrik Elvers during a Practical Course "Beyond Deep Learning - Uncertainty Aware Models" at TU Munich.
        Disclaimer: It is built as an extension to PyTorch and not directly affiliated.
        
        ## Documentation
        A more detailed documentation is included, using the Sphinx framework.
        Go inside directory 'docs' and run 'make html' (having Sphinx installed).
        The documentation can then be found inside the _build sub-directory.
        
        ## Examples
        There are some example models, as well as an evaluation script in the `examples`
        folder.
        
        ## License
        This library is distributed in a ... license.
        
        ## References
        Ian Goodfellow and Yoshua Bengio and Aaron Courville,
        http://www.deeplearningbook.org
        
        Jörg Bornschein, Yoshua Bengio Reweighted Wake-Sleep
        https://arxiv.org/abs/1406.2751
        
        Geoffrey Hinton, A Practical Guide to Training Restricted Boltzmann Machines
        https://www.cs.toronto.edu/~hinton/absps/guideTR.pdf
        
        Ruslan Salakhutdinov, Learning Deep Generative Models
        https://tspace.library.utoronto.ca/handle/1807/19226
        
        Honglak Lee et al., Convolutional Deep Belief Networks for Scalable Unsupervised Learning of Hierarchical
        Representations, ICML09
        
        G.Hinton, S. Osindero A fast learning algorithm for deep belief nets
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
